Text Generation
GGUF
qwen36
Mixture of Experts
conversational
multimodal
agent
ollama
heretic
uncensored
reasoning
distillation
Instructions to use FoolDev/Janus-35B-HERETIC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use FoolDev/Janus-35B-HERETIC with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf FoolDev/Janus-35B-HERETIC:Q4_K_M # Run inference directly in the terminal: llama cli -hf FoolDev/Janus-35B-HERETIC:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf FoolDev/Janus-35B-HERETIC:Q4_K_M # Run inference directly in the terminal: llama cli -hf FoolDev/Janus-35B-HERETIC:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf FoolDev/Janus-35B-HERETIC:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FoolDev/Janus-35B-HERETIC:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf FoolDev/Janus-35B-HERETIC:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FoolDev/Janus-35B-HERETIC:Q4_K_M
Use Docker
docker model run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use FoolDev/Janus-35B-HERETIC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FoolDev/Janus-35B-HERETIC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FoolDev/Janus-35B-HERETIC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
- Ollama
How to use FoolDev/Janus-35B-HERETIC with Ollama:
ollama run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
- Unsloth Desktop
- Pi
How to use FoolDev/Janus-35B-HERETIC with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FoolDev/Janus-35B-HERETIC:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "FoolDev/Janus-35B-HERETIC:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use FoolDev/Janus-35B-HERETIC with Docker Model Runner:
docker model run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
- Lemonade
How to use FoolDev/Janus-35B-HERETIC with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FoolDev/Janus-35B-HERETIC:Q4_K_M
Run and chat with the model
lemonade run user.Janus-35B-HERETIC-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use FoolDev/Janus-35B-HERETIC with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FoolDev/Janus-35B-HERETIC:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default FoolDev/Janus-35B-HERETIC:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use FoolDev/Janus-35B-HERETIC with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FoolDev/Janus-35B-HERETIC:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "FoolDev/Janus-35B-HERETIC:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download scripts/verify_arch.py from FoolDev/Janus-35B-HERETIC: direct link, hf CLI and curl.
- Browser
- Download file 8.06 kB
-
https://huggingface.co/FoolDev/Janus-35B-HERETIC/resolve/main/scripts/verify_arch.py
- Command line
-
hf download hf://FoolDev/Janus-35B-HERETIC/scripts/verify_arch.py
-
curl -L -o verify_arch.py https://huggingface.co/FoolDev/Janus-35B-HERETIC/resolve/main/scripts/verify_arch.py
8.06 kB
| #!/usr/bin/env python3 | |
| """ | |
| Janus-35B — verify the README "Architecture" claims and the MoE | |
| forward-pass structure against the actual GGUF metadata. | |
| Reads the qwen35moe-stamped bundle (or any GGUF that declares that | |
| `general.architecture` value), prints each claim alongside the metadata key | |
| it derives from, and exits non-zero if any value mismatches the expected | |
| claim. Useful as a manual audit after the bundle is re-stamped or after | |
| upstream re-conversion. | |
| Usage: | |
| python3 scripts/verify_arch.py # default bundle | |
| python3 scripts/verify_arch.py Janus-35B-A3B.Q4_K_M.gguf | |
| python3 scripts/verify_arch.py /path/to/some-other.gguf | |
| Exit code 0 = all claims verify, 1 = at least one mismatch, 2 = no readable | |
| GGUF (missing, or an un-smudged git-LFS pointer) / usage error. | |
| Requires: pip install gguf | |
| Note: this does NOT verify the ~34.7B-total / ~3B-active parameter counts | |
| directly (no such KV in the GGUF) — they follow from the layer count, hidden | |
| size and the expert count/width below and llama.cpp's `qwen35moe` type branch, | |
| not from a single metadata field. | |
| `block_count` is checked against 40, which is this model's natural depth (10 | |
| Gated-Attention + 30 Gated-DeltaNet layers), NOT a post-strip value: the | |
| bundled quant carries no MTP/NextN tensors, so scripts/strip_mtp.py is a | |
| defensive no-op for it. llmfan46's separate *-Native-MTP-Preserved-GGUF variant | |
| — which this repo does not ship — reports 41 here, with a NextN block at index | |
| 40 that stock llama.cpp / Ollama cannot load. | |
| """ | |
| from __future__ import annotations | |
| import sys | |
| from pathlib import Path | |
| from gguf import GGUFReader | |
| EXPECTED = { | |
| "block_count": (40, "40 transformer layers"), | |
| "context_length": (262144, "262 144 native context"), | |
| "embedding_length": (2048, "Hidden size 2048"), | |
| "expert_count": (256, "MoE: 256 experts"), | |
| "expert_used_count": (8, "MoE: 8 experts active per token"), | |
| "expert_feed_forward_length": (512, "MoE: per-expert FFN 512"), | |
| "expert_shared_feed_forward_length": (512, "MoE: shared-expert FFN 512"), | |
| "attention.head_count": (16, "Gated Attention: 16 Q-heads"), | |
| "attention.head_count_kv": (2, "Gated Attention: 2 KV-heads (GQA)"), | |
| "attention.key_length": (256, "Gated Attention: head_dim 256 (key)"), | |
| "attention.value_length": (256, "Gated Attention: head_dim 256 (value)"), | |
| "rope.dimension_count": (64, "Partial RoPE: 64 of 256 dims (factor 0.25)"), | |
| "full_attention_interval": (4, "Hybrid stack: every 4th layer is Gated Attention (10 cycles)"), | |
| "ssm.conv_kernel": (4, "Gated DeltaNet: conv kernel 4"), | |
| "ssm.state_size": (128, "Gated DeltaNet: head_dim 128"), | |
| "ssm.time_step_rank": (32, "Gated DeltaNet: 32 V-heads"), | |
| "ssm.group_count": (16, "Gated DeltaNet: 16 QK-heads"), | |
| "ssm.inner_size": (4096, "Gated DeltaNet: inner size 4096 (32 V-heads x 128)"), | |
| } | |
| EXPECTED_VOCAB = 248320 | |
| EXPECTED_ARCHS = {"qwen35moe"} | |
| def read_scalar(reader: GGUFReader, key: str): | |
| f = reader.fields.get(key) | |
| if f is None: | |
| return None | |
| arr = f.parts[f.data[0]] | |
| val = arr.tolist() if hasattr(arr, "tolist") else arr | |
| if isinstance(val, list) and len(val) == 1: | |
| return val[0] | |
| return val | |
| def read_arch(reader: GGUFReader) -> str: | |
| f = reader.fields["general.architecture"] | |
| return bytes(f.parts[f.data[0]]).decode() | |
| def main() -> int: | |
| if len(sys.argv) > 2: | |
| print(f"usage: {sys.argv[0]} [path/to/Janus-35B-A3B.Q4_K_M.gguf]", file=sys.stderr) | |
| return 2 | |
| root = Path(__file__).resolve().parent.parent | |
| default_paths = [ | |
| root / "Janus-35B-A3B.Q4_K_M.gguf", | |
| ] | |
| if len(sys.argv) == 2: | |
| path = Path(sys.argv[1]) | |
| else: | |
| path = next((p for p in default_paths if p.exists() and p.stat().st_size > 1024), None) | |
| if path is None: | |
| # A fresh clone made without git-lfs leaves the bundle on disk as a | |
| # ~136-byte pointer. The size filter above correctly refuses it, but | |
| # "not found" is the wrong diagnosis for a file that is right there - | |
| # load_bundle.sh and live_check.sh both detect this case properly. | |
| pointer = next((p for p in default_paths if p.exists()), None) | |
| if pointer is not None: | |
| head = pointer.open("rb").read(64) | |
| if head.startswith(b"version https://git-lfs.github.com"): | |
| print(f"[!] {pointer} is an un-smudged git-LFS pointer, not the weights.", | |
| file=sys.stderr) | |
| print(" Fetch it with 'git lfs pull', or run ./scripts/load_bundle.sh", | |
| file=sys.stderr) | |
| print(" (it downloads into .cache/ and prints the path), then pass that path.", | |
| file=sys.stderr) | |
| return 2 | |
| print("[!] no Janus-35B GGUF found in repo root; pass a path explicitly", file=sys.stderr) | |
| return 2 | |
| print(f"[*] reading: {path}") | |
| reader = GGUFReader(str(path), "r") | |
| arch = read_arch(reader) | |
| if arch not in EXPECTED_ARCHS: | |
| print(f"[!] unexpected general.architecture: {arch!r} (expected one of {EXPECTED_ARCHS})", file=sys.stderr) | |
| return 1 | |
| print(f"[*] general.architecture: {arch}") | |
| print() | |
| mismatches = 0 | |
| fmt = " {marker} {claim:55s} {key:45s} = {actual}" | |
| for suffix, (expected, claim) in EXPECTED.items(): | |
| key = f"{arch}.{suffix}" | |
| actual = read_scalar(reader, key) | |
| ok = actual == expected | |
| marker = "[ ok ]" if ok else "[FAIL]" | |
| print(fmt.format(marker=marker, claim=claim, key=key, actual=actual)) | |
| if not ok: | |
| mismatches += 1 | |
| # Vocab count comes from the tokenizer tokens array length, not a scalar KV. | |
| f = reader.fields.get("tokenizer.ggml.tokens") | |
| vocab_actual = len(f.data) if f is not None else None | |
| ok = vocab_actual == EXPECTED_VOCAB | |
| marker = "[ ok ]" if ok else "[FAIL]" | |
| print(fmt.format(marker=marker, claim=f"Vocab {EXPECTED_VOCAB}", key="tokenizer.ggml.tokens (length)", actual=vocab_actual)) | |
| if not ok: | |
| mismatches += 1 | |
| # ---- embedded chat template vs the repo's chat_template.jinja ------------- | |
| # Since 0.7.0 this repo ships NO chat_template.jinja: the bundled GGUF carries the | |
| # Qwen 3.6 base's own embedded template, untouched, so there are no two copies to | |
| # drift and the block below skips. It is kept for the case where a maintainer | |
| # reintroduces a repo-side template (as 0.2.0-0.6.5 had): back then the two did | |
| # drift twice, and a stale embedded copy silently reinstates the tool-calling | |
| # failure for every loader that reads the GGUF instead of an external file. | |
| repo_template = Path(__file__).resolve().parent.parent / "chat_template.jinja" | |
| if repo_template.exists(): | |
| f = reader.fields.get("tokenizer.chat_template") | |
| embedded = None | |
| if f is not None and f.parts: | |
| embedded = str(bytes(f.parts[-1]), encoding="utf-8") | |
| expected = repo_template.read_text(encoding="utf-8") | |
| ok = embedded == expected | |
| marker = "[ ok ]" if ok else "[FAIL]" | |
| if embedded is None: | |
| actual = "(absent)" | |
| elif ok: | |
| actual = f"{len(embedded)} chars, identical" | |
| else: | |
| actual = f"{len(embedded)} chars, DIFFERS from the {len(expected)}-char repo file" | |
| print(fmt.format(marker=marker, claim="Embedded chat template == chat_template.jinja", | |
| key="tokenizer.chat_template", actual=actual)) | |
| if not ok: | |
| mismatches += 1 | |
| print(" re-stamp with: python3 scripts/strip_mtp.py IN.gguf OUT.gguf " | |
| "--chat-template chat_template.jinja") | |
| print() | |
| if mismatches: | |
| print(f"[!] {mismatches} mismatch(es) — README Architecture claims disagree with GGUF metadata.") | |
| return 1 | |
| print("[+] all Architecture claims verify against GGUF metadata.") | |
| return 0 | |
| if __name__ == "__main__": | |
| sys.exit(main()) | |